Quantifying uncertainty in the meta-analytic lower bound estimate.

2019 ◽  
Vol 24 (6) ◽  
pp. 754-773 ◽  
Author(s):  
Michael T. Brannick ◽  
Sean Potter ◽  
Yuejia Teng
Keyword(s):  
Lower Bound ◽  
Quantifying Uncertainty ◽  
Lower Bound Estimate

A lower bound estimate of the critical load in bifurcation analysis for incompressible elastic solids

2014 ◽  
Vol 20 (1) ◽  
pp. 53-79 ◽  
Author(s):  
Roger Fosdick ◽  
Pilade Foti ◽  
Aguinaldo Fraddosio ◽  
Salvatore Marzano ◽  
Mario Daniele Piccioni
Keyword(s):  
Lower Bound ◽  
Critical Load ◽  
Bifurcation Analysis ◽  
Elastic Solids ◽  
Lower Bound Estimate

scholarly journals A note on bounded variation and heat semigroup on Riemannian manifolds

2007 ◽  
Vol 76 (1) ◽  
pp. 155-160 ◽  
Author(s):  
A. Carbonaro ◽  
G. Mauceri
Keyword(s):  
Lower Bound ◽  
Euclidean Space ◽  
Heat Kernel ◽  
Ricci Curvature ◽  
Bounded Variation ◽  
Riemannian Manifolds ◽  
Heat Semigroup ◽  
Functions Of Bounded Variation ◽  
Fixed Radius ◽  
Lower Bound Estimate

In a recent paper Miranda Jr., Pallara, Paronetto and Preunkert have shown that the classical De Giorgi's heat kernel characterisation of functions of bounded variation on Euclidean space extends to Riemannian manifolds with Ricci curvature bounded from below and which satisfy a uniform lower bound estimate on the volume of geodesic balls of fixed radius. We give a shorter proof of the same result assuming only the lower bound on the Ricci curvature.

The Use and Evaluation of Shock Spectra in the Dynamic Analysis of Structures

1973 ◽  
Vol 40 (2) ◽  
pp. 433-438
Author(s):  
L. M. Butzel ◽  
H. C. Merchant
Keyword(s):  
Lower Bound ◽  
Empirical Study ◽  
Dynamic Analysis ◽  
Root Mean Square ◽  
Upper Bound ◽  
Dominant Mode ◽  
Maximum Response ◽  
Mean Square ◽  
Lower Bound Estimate

An important use of shock spectra is to make estimates of the maximum responses of linearly modeled multidegree-of-freedom structures to shock excitations. In this paper a lower-bound estimate to complement a well known upper bound on such a maximum response is proposed and examined. The conditions under which the estimate is a lower bound are delineated. The set of bounds is applied to an examination of the performance of two maximum response estimators in current use, the root-mean-square, and one which is a function of the root-mean-square and dominant mode. The results of an empirical study show that the estimators do not perform well except when the bounds are close together.

scholarly journals Whose sample is it anyway? Widespread misannotation of samples in transcriptomics studies

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2103
Author(s):  
Lilah Toker ◽  
Min Feng ◽  
Paul Pavlidis
Keyword(s):  
Lower Bound ◽  
Biomedical Research ◽  
Published Data ◽  
Separate Analysis ◽  
Data Recording ◽  
Expression Levels ◽  
Sample Tracking ◽  
Single Cohort ◽  
Lower Bound Estimate

Concern about the reproducibility and reliability of biomedical research has been rising. An understudied issue is the prevalence of sample mislabeling, one impact of which would be invalid comparisons. We studied this issue in a corpus of human transcriptomics studies by comparing the provided annotations of sex to the expression levels of sex-specific genes. We identified apparent mislabeled samples in 46% of the datasets studied, yielding a 99% confidence lower-bound estimate for all studies of 33%. In a separate analysis of a set of datasets concerning a single cohort of subjects, 2/4 had mislabeled samples, indicating laboratory mix-ups rather than data recording errors. While the number of mixed-up samples per study was generally small, because our method can only identify a subset of potential mix-ups, our estimate is conservative for the breadth of the problem. Our findings emphasize the need for more stringent sample tracking, and that re-users of published data must be alert to the possibility of annotation and labelling errors.

scholarly journals Whose sample is it anyway? Widespread misannotation of samples in transcriptomics studies

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2103 ◽  
Author(s):  
Lilah Toker ◽  
Min Feng ◽  
Paul Pavlidis
Keyword(s):  
Lower Bound ◽  
Biomedical Research ◽  
Published Data ◽  
Separate Analysis ◽  
Data Recording ◽  
Expression Levels ◽  
Sample Tracking ◽  
Single Cohort ◽  
Lower Bound Estimate

Concern about the reproducibility and reliability of biomedical research has been rising. An understudied issue is the prevalence of sample mislabeling, one impact of which would be invalid comparisons. We studied this issue in a corpus of human transcriptomics studies by comparing the provided annotations of sex to the expression levels of sex-specific genes. We identified apparent mislabeled samples in 46% of the datasets studied, yielding a 99% confidence lower-bound estimate for all studies of 33%. In a separate analysis of a set of datasets concerning a single cohort of subjects, 2/4 had mislabeled samples, indicating laboratory mix-ups rather than data recording errors. While the number of mixed-up samples per study was generally small, because our method can only identify a subset of potential mix-ups, our estimate is conservative for the breadth of the problem. Our findings emphasize the need for more stringent sample tracking, and that re-users of published data must be alert to the possibility of annotation and labelling errors.

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